Data-statistics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import parmap\n",
    "import pandas as pd\n",
    "from tqdm import tqdm,tqdm_notebook\n",
    "from utils.marking_similars import *\n",
    "from utils.preprocess import *\n",
    "import seaborn as sns\n",
    "from utils.visualisation import *\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy.random import seed\n",
    "from numpy.random import randn\n",
    "from scipy.stats import mannwhitneyu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "def convert(year,month,date):\n",
    "    return int(datetime(year, month, date, 0, 0, 0).timestamp()*1000)\n",
    "\n",
    "def convert_reverse(timestamp):\n",
    "    dt_object = datetime.fromtimestamp(timestamp/1000)\n",
    "    #print(\"dt_object =\", dt_object)\n",
    "    return dt_object\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "parent_path='Data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open(parent_path+'fear_speech_data.json', 'r') as fp:\n",
    "    fear_speech_data=json.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'message_text': '*प्रशासक समिति*✊🚩  ●●●●●●●●●●● ● ● ● 😎🚩 *आंतकवादी संगठनों का💣🔪 इस्लामिक नाम और उनका इस्लाम.....* *धर्म से जुड़ा हुआ अर्थ...* 🐖🐖🐖 *1.लश्करे तैयबा-फरिश्तो की सेना* *2.अल कायदा-अल्लाह का कायदा...* *3.जेश ए मोहम्द-मोहम्मद साहेब का दल...* *4.तहरिक ए तालिबान-पवित्र योद्धाओ का दल...* *5.हिजबुल मुजादिन-इस्लामी बलिदानियो का समूह...* *6.बोको हराम -पैगम्बर मुहम्मद की शिक्षा को फैलाने के लिए प्रतिबद्ध..* *सभी मुस्लमान अज्ञानि अल्लाह की बताई हुई रूहानी किताब क़ुरआन की बताई राह* *(पूरी दुनिया को इस्लाम बनाना)पर ही चल रहे है कोई ज्यादा बच👨\\u200d👨\\u200d👦* *पैदा करके तो कोई लव जिहाद👫 करके तो कोई काफ़िर(गेर मुसलमान)को मारकर..* *धरती पर आंतक🔫💣💣💣🔫फेला रहे है\\ufeff।।* 😡😡😡😎😡😡😡  *जय सनातन धर्म की*🚩🚩🚩  🙏🚩🇮🇳🔱🏹🐚🕉',\n",
       " 'annotation_list': ['Fear speech', 'Fear speech', 'Normal'],\n",
       " 'propagation': [{'group_id': 9087,\n",
       "   'user_id': 229869,\n",
       "   'timestamp': 1538130086000},\n",
       "  {'group_id': 7, 'user_id': 215, 'timestamp': 1550186113000}]}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fear_speech_data['0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "repeat_tuples=[]\n",
    "for key in fear_speech_data.keys():\n",
    "    element = fear_speech_data[key]\n",
    "    \n",
    "    count_fearspeech=element['annotation_list'].count('Fear speech')\n",
    "    count_normal=element['annotation_list'].count('Normal')\n",
    "    \n",
    "    if(count_fearspeech>count_normal):\n",
    "        one_fear_speech=1\n",
    "    else:\n",
    "        one_fear_speech=0\n",
    "    \n",
    "    try:\n",
    "        #### timestamps\n",
    "        time_stamp_list = []\n",
    "        for ele in element['propagation']:\n",
    "            time_stamp_list.append(ele['timestamp'])\n",
    "        time_stamp_list.sort()\n",
    "\n",
    "        ##starting date\n",
    "        timestamp=time_stamp_list[0]\n",
    "        date=convert_reverse(timestamp)\n",
    "\n",
    "        if(date.year==2019 and date.month>5):\n",
    "            observed=0\n",
    "        else:\n",
    "            observed=1\n",
    "\n",
    "\n",
    "        if(one_fear_speech==1):\n",
    "            repeat_tuples.append([timestamp,observed,len(element['propagation']),'Fear speech'])\n",
    "        else:\n",
    "            repeat_tuples.append([timestamp,observed,len(element['propagation']),'Non Fear speech'])\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_repeat=pd.DataFrame(repeat_tuples,columns=['timestamp','observed','num_repeats','label'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=1697844.000, p=0.00062\n",
      "Different distribution (reject H0)\n"
     ]
    }
   ],
   "source": [
    "# generate two independent samples\n",
    "# compare samples\n",
    "stat, p = mannwhitneyu(df_repeat[df_repeat['label']=='Fear speech'].num_repeats, df_repeat[df_repeat['label']=='Non Fear speech'].num_repeats)\n",
    "print('Statistics=%.3f, p=%.5f' % (stat, p))\n",
    "# interpret\n",
    "alpha = 0.05\n",
    "if p > alpha:\n",
    "\tprint('Same distribution (fail to reject H0)')\n",
    "else:\n",
    "\tprint('Different distribution (reject H0)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (5, 5)\n",
    "\n",
    "#     'weight' : 'normal',\n",
    "#     'size'   : 15}\n",
    "# matplotlib.rc('font', **font)\n",
    "#data_timeline.plot()\n",
    "sns.set(context='paper',style='white',font_scale=1.5)\n",
    "\n",
    "font = {\n",
    "    'weight' : 'normal',\n",
    "    'size'   : 11}\n",
    "ax = sns.barplot(x=\"label\", y=\"num_repeats\", data=df_repeat)\n",
    "#g=add_values(g,df_admin,15,order_list)\n",
    "plt.savefig('../Fear_speech_results/number_of_repeats.pdf',bbox_inches='tight',dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_groups_affected=[]\n",
    "for key in fear_speech_data.keys():\n",
    "    element = fear_speech_data[key]\n",
    "    \n",
    "    count_fearspeech=element['annotation_list'].count('Fear speech')\n",
    "    count_normal=element['annotation_list'].count('Normal')\n",
    "    \n",
    "    if(count_fearspeech>count_normal):\n",
    "        one_fear_speech=1\n",
    "    else:\n",
    "        one_fear_speech=0\n",
    "    \n",
    "    group_list = []\n",
    "    for ele in element['propagation']:\n",
    "            group_list.append(ele['group_id'])\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    if(one_fear_speech==1):\n",
    "        num_groups_affected.append([len(set(group_list)),'Fear speech'])\n",
    "    else:\n",
    "        num_groups_affected.append([len(set(group_list)),'Non Fear speech'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=1921507.000, p=0.0000265482\n",
      "Different distribution (reject H0)\n"
     ]
    }
   ],
   "source": [
    "df_num_groups=pd.DataFrame(num_groups_affected,columns=['groups_affected','label'])\n",
    "\n",
    "# generate two independent samples\n",
    "# compare samples\n",
    "stat, p = mannwhitneyu(df_num_groups[df_num_groups['label']=='Fear speech'].groups_affected, df_num_groups[df_num_groups['label']=='Non Fear speech'].groups_affected)\n",
    "print('Statistics=%.3f, p=%.10f' % (stat, p))\n",
    "# interpret\n",
    "alpha = 0.05\n",
    "if p > alpha:\n",
    "\tprint('Same distribution (fail to reject H0)')\n",
    "else:\n",
    "\tprint('Different distribution (reject H0)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (5, 5)\n",
    "\n",
    "#     'weight' : 'normal',\n",
    "#     'size'   : 15}\n",
    "# matplotlib.rc('font', **font)\n",
    "#data_timeline.plot()\n",
    "sns.set(context='paper',style='white',font_scale=1.5)\n",
    "\n",
    "font = {\n",
    "    'weight' : 'normal',\n",
    "    'size'   : 11}\n",
    "ax1 = sns.barplot(x=\"label\", y=\"groups_affected\", data=df_num_groups)#g=add_values(g,df_admin,15,order_list)\n",
    "plt.savefig('../Fear_speech_results/groups_affected.pdf',bbox_inches='tight',dpi=400)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_spreader=[]\n",
    "for key in fear_speech_data.keys():    \n",
    "    element = fear_speech_data[key]\n",
    "    count_fearspeech=element['annotation_list'].count('Fear speech')\n",
    "    count_normal=element['annotation_list'].count('Normal')\n",
    "    \n",
    "    if(count_fearspeech>count_normal):\n",
    "        one_fear_speech=1\n",
    "    else:\n",
    "        one_fear_speech=0\n",
    "    \n",
    "    timestamp_list = []\n",
    "    for ele in element['propagation']:\n",
    "            timestamp_list.append(ele['timestamp'])\n",
    "    \n",
    "    \n",
    "    try:\n",
    "        days_active = (timestamp_list[-1] - timestamp_list[0])/(24*60*60*1000)\n",
    "        \n",
    "    except:\n",
    "        days_active = 0\n",
    "    \n",
    "    if(one_fear_speech==1):\n",
    "        num_spreader.append([days_active,'Fear speech'])\n",
    "    else:\n",
    "        num_spreader.append([days_active,'Non Fear speech'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=1896897.000, p=0.0000009504\n",
      "Different distribution (reject H0)\n"
     ]
    }
   ],
   "source": [
    "df_active=pd.DataFrame(num_spreader,columns=['days_active','label'])\n",
    "\n",
    "\n",
    "# generate two independent samples\n",
    "# compare samples\n",
    "stat, p = mannwhitneyu(df_active[df_active['label']=='Fear speech'].days_active, df_active[df_active['label']=='Non Fear speech'].days_active)\n",
    "print('Statistics=%.3f, p=%.10f' % (stat, p))\n",
    "# interpret\n",
    "alpha = 0.05\n",
    "if p > alpha:\n",
    "\tprint('Same distribution (fail to reject H0)')\n",
    "else:\n",
    "\tprint('Different distribution (reject H0)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (5, 5)\n",
    "\n",
    "#     'weight' : 'normal',\n",
    "#     'size'   : 15}\n",
    "# matplotlib.rc('font', **font)\n",
    "#data_timeline.plot()\n",
    "sns.set(context='paper',style='white',font_scale=1.5)\n",
    "\n",
    "font = {\n",
    "    'weight' : 'normal',\n",
    "    'size'   : 11}\n",
    "ax1 = sns.barplot(x=\"label\", y=\"days_active\", data=df_active)\n",
    "plt.savefig('../Fear_speech_results/number_of_days_active.pdf',bbox_inches='tight',dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_spreader=[]\n",
    "for key in fear_speech_data.keys():\n",
    "    element = fear_speech_data[key]\n",
    "    count_fearspeech=element['annotation_list'].count('Fear speech')\n",
    "    count_normal=element['annotation_list'].count('Normal')\n",
    "    \n",
    "    if(count_fearspeech>count_normal):\n",
    "        one_fear_speech=1\n",
    "    else:\n",
    "        one_fear_speech=0\n",
    "    \n",
    "    user_list = []\n",
    "    for ele in element['propagation']:\n",
    "            user_list.append(ele['user_id'])\n",
    "  \n",
    "    if(one_fear_speech==1):\n",
    "        num_spreader.append([len(set(user_list)),'Fear speech'])\n",
    "    else:\n",
    "        num_spreader.append([len(set(user_list)),'Non Fear speech'])\n",
    "\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=1874754.500, p=0.0000000412\n",
      "Different distribution (reject H0)\n"
     ]
    }
   ],
   "source": [
    "df_num_users=pd.DataFrame(num_spreader,columns=['users_who_spread','label'])\n",
    "\n",
    "# generate two independent samples\n",
    "# compare samples\n",
    "stat, p = mannwhitneyu(df_num_users[df_num_users['label']=='Fear speech'].users_who_spread, df_num_users[df_num_users['label']=='Non Fear speech'].users_who_spread)\n",
    "print('Statistics=%.3f, p=%.10f' % (stat, p))\n",
    "# interpret\n",
    "alpha = 0.05\n",
    "if p > alpha:\n",
    "\tprint('Same distribution (fail to reject H0)')\n",
    "else:\n",
    "\tprint('Different distribution (reject H0)')\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (5, 5)\n",
    "\n",
    "#     'weight' : 'normal',\n",
    "#     'size'   : 15}\n",
    "# matplotlib.rc('font', **font)\n",
    "#data_timeline.plot()\n",
    "sns.set(context='paper',style='white',font_scale=1.5)\n",
    "\n",
    "font = {\n",
    "    'weight' : 'normal',\n",
    "    'size'   : 11}\n",
    "ax1 = sns.barplot(x=\"label\", y=\"users_who_spread\", data=df_num_users)\n",
    "plt.savefig('../Fear_speech_results/users_who_spread.pdf',bbox_inches='tight',dpi=400)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.lines import Line2D\n",
    "from  matplotlib.ticker import FuncFormatter\n",
    "matplotlib.rcParams['figure.figsize'] = (10, 6)\n",
    "\n",
    "#     'weight' : 'normal',\n",
    "#     'size'   : 15}\n",
    "# matplotlib.rc('font', **font)\n",
    "#data_timeline.plot()\n",
    "sns.set(context='paper',style='white',font_scale=1.5)\n",
    "\n",
    "font = {\n",
    "    'weight' : 'normal',\n",
    "    'size'   : 11}\n",
    "\n",
    "ax1=plt.subplot(2, 2, 1)\n",
    "ax = sns.barplot(x=\"label\", y=\"num_repeats\",ax=ax1,data=df_repeat)\n",
    "plt.tick_params(\n",
    "    axis='x',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=False) # labels along the bottom edge are off\n",
    "plt.ylabel(\"Avg. reshares\")\n",
    "plt.xlabel(\"(a)\")\n",
    "plt.gca().yaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))\n",
    "\n",
    "\n",
    "ax2=plt.subplot(2, 2, 2)\n",
    "ax = sns.barplot(x=\"label\", y=\"users_who_spread\",ax=ax2, data=df_num_users)\n",
    "plt.tick_params(\n",
    "    axis='x',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=False) # labels along the bottom edge are off\n",
    "plt.ylabel(\"Avg. spreading users\")\n",
    "\n",
    "plt.xlabel(\"(b)\")\n",
    "\n",
    "ax3=plt.subplot(2, 2, 3)\n",
    "ax = sns.barplot(x=\"label\", y=\"groups_affected\",ax=ax3, data=df_num_groups)\n",
    "plt.tick_params(\n",
    "    axis='x',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=False) # labels along the bottom edge are off\n",
    "plt.ylabel(\"Avg. groups affected\")\n",
    "\n",
    "plt.xlabel(\"(c)\")\n",
    "\n",
    "ax4=plt.subplot(2, 2, 4)\n",
    "ax = sns.barplot(x=\"label\", y=\"days_active\",ax=ax4, data=df_active)\n",
    "plt.tick_params(\n",
    "    axis='x',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=False) # labels along the bottom edge are off\n",
    "plt.ylabel(\"Avg. days active\")\n",
    "\n",
    "plt.xlabel(\"(d)\")\n",
    "\n",
    "cmap=matplotlib.cm.get_cmap(name='tab20c')\n",
    "\n",
    "custom_lines = [Line2D([0], [0], color=sns.color_palette()[0], lw=6),\n",
    "                Line2D([0], [0], color=sns.color_palette()[1], lw=6)]\n",
    "\n",
    "\n",
    "plt.legend(custom_lines, ['Fear speech', 'Non fear speech'],bbox_to_anchor=(0.5, 2.5), ncol=2)\n",
    "#plt.legend(labels=['fear speech','non fear speech'], loc='center')\n",
    "plt.savefig('../Fear_speech_results/total.pdf',bbox_inches='tight',dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "groups 4.599725274725275\n",
      "days active 15.459084745115995\n",
      "re shares 5.6899940440738535\n",
      "users who spread 3.8315934065934067\n"
     ]
    }
   ],
   "source": [
    "print('groups',np.mean(df_num_groups[df_num_groups['label']=='Non Fear speech']['groups_affected']))\n",
    "print('days active',np.mean(df_active[df_active['label']=='Non Fear speech']['days_active']))\n",
    "print('re shares',np.mean(df_repeat[df_repeat['label']=='Non Fear speech']['num_repeats']))\n",
    "print('users who spread',np.mean(df_num_users[df_num_users['label']=='Non Fear speech']['users_who_spread']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "groups 5.996497373029772\n",
      "days active 19.925839434552763\n",
      "re shares 7.277365491651206\n",
      "users who spread 5.361646234676007\n"
     ]
    }
   ],
   "source": [
    "print('groups',np.mean(df_num_groups[df_num_groups['label']=='Fear speech']['groups_affected']))\n",
    "print('days active',np.mean(df_active[df_active['label']=='Fear speech']['days_active']))\n",
    "print('re shares',np.mean(df_repeat[df_repeat['label']=='Fear speech']['num_repeats']))\n",
    "print('users who spread',np.mean(df_num_users[df_num_users['label']=='Fear speech']['users_who_spread']))"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python [conda env:.conda-punyajoy_gpu] *",
   "language": "python",
   "name": "conda-env-.conda-punyajoy_gpu-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": true,
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   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
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   "labels_anchors": false,
   "latex_user_defs": false,
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